CN116307403A - Planting process recommendation method and system based on digital village - Google Patents
Planting process recommendation method and system based on digital village Download PDFInfo
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Abstract
The invention provides a planting process recommending method and system based on a digital country, wherein the method comprises the following steps: evaluating the planting capacity of each household in the country by using two indexes of the acre yield of the agricultural products under the same land grade and the hourly yield of the planted agricultural products, and screening out the planting energy of each household in the country; taking the planting process of the planting energy families of various agricultural products as an optimal planting process, extracting the time sequence of the optimal planting process and generating an optimal planting process sequence; recommending the optimal planting process sequence to other families for planting corresponding agricultural products in the village, and comparing and analyzing the optimal planting process sequence to remind the families which do not accord with the optimal planting process sequence to correct the agricultural activities. According to the invention, families with strong planting capacity are screened out from the last year, the optimal planting process sequence is formed, the families with weak planting capacity are recommended to the families in the village, the planting process is regulated in time, the planting process can be controlled more intuitively and finely, and the learning difficulty of the planting process is reduced.
Description
Technical Field
The invention belongs to the technical field of data recommendation, and particularly relates to a planting process recommendation method and system based on a digital country.
Background
The digital village is an application in the development of agriculture rural economy and society along with networking, informatization and digitalization, and is an important means for the modern development and transformation of agriculture rural. Depending on the digital rural technology, problems of what products to plant, lack of planting experience, how to improve economic benefits and the like are all gradually solved. However, for the problems of lack of planting experience and planting skills, the existing digital rural technology basically relies on an expert to remotely consult or find the expert to conduct on-site guidance, and the method has a certain improvement effect on scientific planting, but for wide agricultural production activities in China, the expert guidance cannot be conducted anytime and anywhere and is difficult to cover various basic layers, so that the method of the expert guidance has a plurality of defects. In addition, due to the differences of regions and climates, the planting experience and planting skill are required to be suitable according to the local conditions, and cannot be generalized. Therefore, for each country, a more careful and timely standard planting scheme is required according to local conditions.
The invention patent with publication number of CN106599136A discloses a method and a device for guiding agricultural production based on big data, which are used for analyzing the planting period, average yield and average price of each crop through data mining and pushing proper planted crops to a planter, thereby providing a more scientific planting scheme and avoiding blind planting. The scheme can be suitable for recommending the planted crops, but cannot scientifically and effectively guide the detailed planting process, so that the defect of the planting process is difficult to timely make up.
Therefore, a digital rural-based planting process recommendation scheme is needed, so that farmers in each rural area can learn the standard and reasonable planting process more conveniently.
Disclosure of Invention
In view of the above, the invention provides a planting process recommending method and system based on a digital village, which are used for solving the problem that the existing digital village technology cannot timely standardize the agricultural product planting process of each household.
The invention discloses a planting process recommending method based on a digital village, which comprises the following steps:
evaluating the planting capacity of each household in the village by using two indexes of the acre yield of the agricultural products and the hourly yield of the planted agricultural products, and screening out the planting energy families of various agricultural products in the village;
taking the planting process of the planting energy families of various agricultural products as an optimal planting process, extracting the time sequence of the optimal planting process and generating an optimal planting process sequence;
recommending the optimal planting process sequence to other families for planting corresponding agricultural products in the village, and comparing and analyzing the optimal planting process sequence to remind the families which do not accord with the optimal planting process sequence to correct the agricultural activities.
On the basis of the technical scheme, preferably, the method for evaluating the planting capacity of each household in the rural area by using two indexes of the acre yield of the agricultural products and the hourly yield of the planted agricultural products, and screening the planting capacity of each household in the rural area specifically comprises the following steps:
calculating average acre yield and average hourly yield of each household by utilizing the agricultural product sales data of the last year and household agricultural activity records for certain agricultural products;
calculating the planting capacity index of each family according to the average mu yield and the average hourly yield of each familyIndex：
Wherein, the liquid crystal display device comprises a liquid crystal display device,prepresents the average acre yield of the corresponding agricultural products planted in one family,
represents the average mu yield of the corresponding agricultural products planted in the country, < >>
Is the average hourly yield of a household planting of the corresponding agricultural product,/->
Representing the average hourly yield of planting the corresponding agricultural product in the country; />
Is the weight coefficient of average mu yield and ∈Ten->
Is a weight coefficient for average hourly production; for the planting ability indexIndexSorting, and screening households with the largest planting capacity indexes as planting energy households.
On the basis of the above technical solution, preferably, calculating the average acre yield and average hourly yield of each household by using the last year agricultural product sales data and the household agricultural activity records specifically includes:
collecting the agronomic activity records of each peasant, and storing the records in the form of a data table structure, wherein the records comprise family numbers, land numbers, family members, agronomic activity starting time and agronomic activity ending time;
summarizing labor time length by land number;
collecting land production records and storing the records in the form of a data table structure, wherein the records comprise family numbers, land numbers, agricultural products, areas and total yield;
the land number is used for associating the agricultural activity record, the labor duration and the land production record to form result data, and each piece of result data records the labor duration and the agricultural product yield data of a certain land of a family contractor;
according to the result data, gathering agronomic activity records by taking families as units, and calculating average acre yield and average hourly yield of each family; the average hourly production is equal to the total production divided by the total labor duration.
On the basis of the above technical solution, preferably, the extracting the time sequence of the optimal planting process and generating the optimal planting process sequence specifically includes:
the agricultural activity records of the corresponding agricultural products of the planting energy families are ordered according to the ascending order of time;
selecting two adjacent agronomic activity records in the sorted agronomic activity records, wherein the former record is reference data, the latter record is subsequent data, comparing the reference data with the subsequent data, and judging whether the agronomic activity is the same or not and the time difference between the two records is not more than 2 days;
if the same agronomic activity is performed and the time difference is not more than 2 days, two agronomic activity records belong to the same planting sequence, and a planting sequence is generated in a data accumulation mode; if the two agronomic activity records are different, the two agronomic activity records are respectively divided into different planting sequences;
and repeating the above processes to perform sequential cyclic judgment, forming all planting sequences for planting the corresponding agricultural products from the agronomic activity records, and synthesizing all the planting sequences to form an optimal planting process sequence.
On the basis of the above technical solution, preferably, the generating a planting sequence in the form of data accumulation specifically includes:
calculating labor time and agricultural material consumption from the reference data, and attaching the land number, the labor time and the agricultural material consumption in the reference data to corresponding data in the subsequent data; the labor time and the agricultural material consumption are directly added to the corresponding data, and if the land numbers are different, the land numbers are added after the land numbers of the subsequent data through the separator;
the number of land acres is obtained through land numbering, the labor capacity per acre and the agricultural material consumption per acre are calculated, and a planting sequence is generated.
On the basis of the above technical solution, preferably, the recommending the optimal planting process sequence to other households in the country for planting the corresponding agricultural products specifically includes:
and selecting a family with a planting capacity Index of <1 as a family with weak planting capacity, and pushing a planting process comparison image of the optimal planting process sequence and the family planting process sequence to the family with weak planting capacity in a form of subscribing message service.
On the basis of the above technical solution, preferably, the comparing and analyzing to remind the family not conforming to the optimal planting process sequence to correct the agronomic activity specifically includes:
real-time monitoring the agronomic activity process of the corresponding agricultural products of families with weak planting capacity, and judging whether the family meets the optimal planting process sequence according to the time interval between agronomic activities and the condition of various agronomic resource consumption in the agronomic activities;
and carrying out planting non-standard early warning on families which do not accord with the optimal planting process sequence, and reminding the corresponding families to correct the planting process.
In a second aspect of the present invention, a digital rural-based planting process recommendation system is disclosed, the system comprising:
and the planting energy hand screening module: the method is used for evaluating the planting capacity of each household in the village by using two indexes of the acre yield of the agricultural products and the hourly yield of the planted agricultural products, and screening out the planting energy families of various agricultural products in the village;
and a planting process extraction module: the method comprises the steps of taking a planting process of planting energy families of various agricultural products as an optimal planting process, extracting a time sequence of the optimal planting process and generating an optimal planting process sequence;
and a planting process recommending module: the method is used for recommending the optimal planting process sequence to other families for planting corresponding agricultural products in the village, comparing and analyzing the optimal planting process sequence, and reminding the families which do not accord with the optimal planting process sequence to correct the agricultural activities.
Compared with the prior art, the invention has the following beneficial effects:
1) According to the invention, based on the characteristics that the geography and climate environment of the land under the same land grade are the same and the mu yield of agricultural products is positively correlated with the family planting capacity in the same country, a planting process recommendation scheme based on the digital country is provided, families with strong planting capacity are screened out from the previous year, the families with weak planting capacity are planted by taking the planting process as a standard in the next year planting process, and simultaneously, the agronomic activities of the families with weak planting capacity are monitored, and once the families are inconsistent with the template, the improvement of the families is notified and the planting process is timely standardized, so that the information sharing capacity of the planting process in the digital country is improved, and the yield of the agricultural products is improved;
2) According to the invention, the planting capacity of each household is comprehensively evaluated through the two indexes of the acre yield and the per-hour yield of the planted agricultural products, the planting energy families considering acre yield and working efficiency can be screened out and the optimal planting process recommendation is carried out, so that the acre yield in the recommended planting process can be ensured to be highest, and the working efficiency is highest at the same time, thereby being beneficial to improving the acre yield and the working efficiency of the whole village;
3) According to the invention, through processing and analyzing the agronomic activity records of the families with the planting energy, a standard planting process sequence can be integrated, and compared and analyzed with families with weak planting capacity, whether the planting process sequence accords with the optimal planting process sequence or not is judged according to the time interval between the families with weak planting capacity and the condition of various agronomic resources in the agronomic activities, and for the non-conforming agronomic activities, a control image is pushed to remind, so that the planting process can be managed and controlled more intuitively and finely, and meanwhile, the learning difficulty of the planting process is reduced.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a digital rural based planting process recommendation method of the present invention;
FIG. 2 is a schematic diagram of a historical data processing process;
FIG. 3 is a flow chart for calculating average acre yield and average hourly yield per household;
FIG. 4 is a graph showing the computing power indexIndexAnd a flow chart of the sequencing;
FIG. 5 is a flow chart for generating a sequence of optimal planting processes.
Description of the embodiments
The following description of the embodiments of the present invention will clearly and fully describe the technical aspects of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
The yield of agricultural products on each land in the country and the labor record of each household in land cultivation can be recorded in the digital country, so that the agricultural activity records of each household and each land can be conveniently obtained. In the same country, the geography and climate environment of the land under the same land grade are the same, and the acre yield of agricultural products is positively related to the family planting capacity. According to the characteristic, the invention combines the convenience of acquiring data by the digital village, screens out families with strong planting capacity according to the output data of agricultural products in families in the last year and records of agricultural activities, takes the planting process as a standard, and recommends the families with weak planting capacity to the families with weak planting capacity in the planting process in the next year so as to standardize the planting process and improve the output of agricultural products.
Referring to fig. 1, the invention provides a planting process recommending method based on a digital country, which comprises the following steps:
s1, evaluating the planting capacity of each household in the country by using two indexes, namely the acre yield of the agricultural products and the hourly yield of the planted agricultural products, and screening out the planting energy of each agricultural product in the country.
The planting capacity of a family for certain agricultural products is reflected in two aspects, namely the yield per mu and the working efficiency. The invention uses the output data of agricultural products in families and records of agricultural activities to count the total output of a certain type of agricultural products in the same land level in one year and the total labor time of each family when planting the agricultural products. And then calculating average acre yield by calculating the acre number and the total yield, and calculating average yield of families per hour by the ratio of the total yield to the total labor duration. Thus, average acre yield is indicative of the capacity of a household to produce a certain type of agricultural product, and average hourly yield is indicative of the efficiency of the household to plant that type of agricultural product. The two indexes of the per mu yield and the per hour yield of the planted agricultural products are comprehensively utilized to screen the planting energy of a certain type of agricultural products in a country.
The step S1 specifically comprises the following sub-steps:
s11, processing historical data: yield data of each land and records of agricultural activities on each land in the last year are collected from the digital village and processed.
As shown in fig. 2, the step S11 specifically includes the following sub-steps:
s111, collecting the agronomic activity records of each peasant, and storing the records in a data table structure.
The method comprises the steps of family numbering, land numbering, family members, farm work start time and farm work end time, each data record the condition that one family member engages in farm work production on one land, and the method enters the substep S112 after completion.
S112, summarizing labor time with land numbers,
and calculating the activity duration of each record based on the farm activity time and the end time, and then summarizing the labor duration of all people by taking land numbers as units. The results formed included land number and labor duration, each record being the total duration of the agronomic activity being undertaken on a piece of land in hours. After completion, the process advances to sub-step S113.
S113, collecting the land generation records and storing the records in a data table structure mode.
And collecting all the land yield records of all households, wherein the land yield records comprise household numbers, land numbers, agricultural products, areas and total yield. After completion, sub-step S114 is entered.
And S114, associating the farm work activity record, the labor duration and the land production record through land numbers to form result data.
The associated data information includes family number, land number, agricultural product, area, total yield and total labor duration. Each result data records the labor duration and agricultural product yield data of a certain land of a home contractor.
After the history data processing is completed, the process proceeds to step S12.
And S12, calculating the average mu yield and the average hourly yield of each family.
The average acre yield and the average hourly yield of the village are calculated, the agricultural activity records are gathered by taking families as units, and the average acre yield and the average hourly yield of each family are calculated.
As shown in fig. 3, a flowchart for calculating average acre yield and average hourly yield of each household is shown, and step S12 specifically includes the following sub-steps:
s121, selecting agricultural products and filtering data.
Among the various agricultural products planted in the country, a certain type of agricultural product is clarified, and data which is not the type of agricultural product is filtered from the processing result of the historical data in step S11, and the remaining data is data generated by planting the agricultural product in the country. After completion, sub-step S122 is entered.
S122, aggregating all the data and calculating average acre yield and average hourly yield.
And counting the total yield, the total planting acre and the total labor duration of all families in the village according to the filtered data. Dividing the total yield by the total planting acre to obtain the average acre yield. Dividing the total throughput by the total labor duration gives the average hourly throughput. After completion, the process proceeds to sub-step S123.
And S123, gathering data by taking families as units, and calculating average mu yield and average hourly yield of the families.
And (3) sorting the filtered data in the step (S12) according to family numbers, and selecting all data of a family, wherein each piece of data represents data generated by planting the agricultural products on a certain land by the family. The total yield, total acre and total labor duration in the data are gathered, and then the average acre yield and average hourly yield of the household are calculated respectively, wherein the 2 values represent the capability of the household to plant the agricultural products. After completion, the process proceeds to substep S124.
S124, judging whether the data calculation of the families planted with the agricultural products is finished.
If all the family traversals are completed, all the calculation results are saved, the data comprise family numbers, agricultural products, average acre yield and average hourly yield, and each data reflects the planting capacity of each family for planting the agricultural products. Otherwise, the process returns to substep S123.
After all sub-steps are completed, the process proceeds to step S13.
S13, calculating capability indexIndexAnd ordered.
As shown in FIG. 4, the computing power indexIndexAnd the sequencing flow chart, the step S13 specifically includes the following sub-steps:
s131, selecting one piece of family data, and calculating the planting capacity index of each family according to the average acre yield and the average hourly yield of each familyIndex：
Wherein, the liquid crystal display device comprises a liquid crystal display device,prepresents the average acre yield of the corresponding agricultural products planted in one family,
represents the average mu yield of the corresponding agricultural products planted in the country, < >>
Is the average hourly yield of a household planting of the corresponding agricultural product,/->
Representing the average hourly yield of planting the corresponding agricultural product in the country; />
Is the weight coefficient of average mu yield and ∈Ten->
Is a weight coefficient for average hourly production; />
And->
The value range is 0-1. Its value satisfies->
. The calculated result is correlated with the piece of family data, and the final data structure is family number, average acre yield, average hourly yield and planting capacity index.
S132, traversing all family data and calculating the planting capacity index to obtain the planting capacity index of multiple familiesIndex。
S132, planting capacity indexIndexSorting, and screening households with the largest planting capacity indexes as planting energy households.
Each household is ranked according to the planting ability index value from large to small. The results of the ranking reflect the ranking of all home competence for planting such agricultural products. And (5) screening the family with the largest planting capacity index as a planting energy hand family.
According to the invention, the planting capacity of each household is comprehensively evaluated through the two indexes of the acre yield and the per-hour yield of the planted agricultural products, the planting energy families considering acre yield and working efficiency can be screened out, the optimal planting process recommendation is carried out, the acre yield in the recommended planting process can be ensured to be highest, the working efficiency is highest, and the acre yield and the working efficiency of the whole village are improved.
S2, taking the planting process of the planting energy families of various agricultural products as an optimal planting process, extracting the time sequence of the optimal planting process and generating an optimal planting process sequence.
And taking the planting process of the planting energy family as an optimal planting process, extracting the agronomic activity records of the planting energy family in planting the agricultural products, and sequencing the agronomic activity records of the corresponding agricultural products of the planting energy family according to time ascending sequence. And selecting two adjacent agronomic activity records in the sorted agronomic activity records, wherein the former record is reference data and the latter record is follow-up data.
Comparing the reference data with the subsequent data, judging whether the agronomic activities are the same and the difference between the two recorded times is not more than 2 days; if the same agronomic activity is performed and the time difference is not more than 2 days, two agronomic activity records belong to the same planting sequence, and a planting sequence is generated in a data accumulation mode; calculating labor time and agricultural material consumption from the reference data, and attaching the land number, the labor time and the agricultural material consumption in the reference data to corresponding data in the subsequent data; the labor time and the agricultural material consumption are directly added to the corresponding data, and if the land numbers are different, the land numbers are added after the land numbers of the subsequent data through the separator; the number of land acres is obtained through land numbering, the labor capacity per acre and the agricultural material consumption per acre are calculated, and a planting sequence is generated. If the agronomic activities are different, indicating that the two agronomic activity records are different agronomic activities to be engaged in, and dividing the two agronomic activity records into different planting sequences respectively;
and repeating the above processes to perform sequential cyclic judgment, forming all planting sequences for planting the corresponding agricultural products from the agronomic activity records, and synthesizing all the planting sequences to form an optimal planting process sequence.
As shown in fig. 5, which is a flowchart of the best planting process sequence, step S2 specifically includes the following sub-steps:
sub-step 1) acquiring agronomic activity records: in rural farming records, all farming records of the household for planting such agricultural products are selected. After the data acquisition is completed, a substep 2) is entered.
Substep 2) ordering in time: the data obtained in the sub-step 2) are ordered in ascending order of time, the time of the first line of data is the earliest, and the time of the last line of data is the latest. And (3) entering a substep after completion).
Substep 3) setting sequence variablesi=1: sequence variableiThe initial value of the sequence is 1, and is mainly used for indicating the sequence of the planting. After completion, sub-step 4) is entered.
Substep 4) selecting the first row as reference data: the data of the first row is selected as reference data, which is to be compared with the subsequent data. After completion, substep 5) is entered.
Substep 5) selecting the next row in order as the subsequent data: and selecting the next piece of data as the subsequent data, and preparing for comparison with the reference data. After completion, sub-step 6) is entered.
Substep 6) compares whether the reference data and the subsequent data are similar: the comparison method is that whether the agricultural activities are the same is firstly judged, if the agricultural activities are the same, whether the time difference with the follow-up data is within 2 days is further judged, if the time difference with the follow-up data is within 2 days, the judgment result is yes, then the substep 7) is carried out, and if the time difference with the follow-up data is not more than 2 days, otherwise the judgment result is no, the substep 9 is carried out.
Substep 7) accumulating the data into the subsequent data: and calculating the labor time and the agricultural material amount from the reference data, and attaching the land number, the labor time and the agricultural material amount in the reference data to corresponding data in the follow-up data. The labor time length and the agricultural material amount are directly added to the corresponding data, the land number of the subsequent data is added through a separator (such as comma), and if the subsequent land number exists, the land number does not need to be added. After completion, substep 8) is entered.
Sub-step 8) setting the subsequent data as reference data: setting the subsequent data as reference data, wherein the labor time length and the agricultural material amount in the previous reference data are accumulated, and the reference data need to be reset. After completion, substep 5) is entered.
And 9) calculating the labor amount and the agricultural material consumption per mu: in the reference data, the land acre number is obtained through land numbering; if comma separated acres exist, the acres of all land numbers are accumulated, and then the labor amount and the agricultural material consumption of each acre are calculated. After completion the sub-step 10) is entered.
Substep 10) generating the firstiAnd (3) strip planting sequences: and removing the year, and only reserving data with month and date, wherein if the data is the first sequence, the time interval is 0, and if the data is not the first sequence, the interval is the difference between the reference data and the last planting sequence, and other data are derived from the calculated data of the corresponding column of the reference data. After completion the sub-step 11) is entered.
Sub-step 11) setting the subsequent data as reference data: the previous reference data has become the original data of the planting sequence, and the subsequent data is set as the reference data. After completion the sub-step 13) is entered.
Substep 12) is whether there is further data: judging whether data exist, if not, indicating that the reference data are the last piece of data, entering the substep 14), otherwise, entering the substep 5).
Substep 13i=i+1: finish the first stepiAnd (3) planting the strips, adding a new sequence, and entering a substep 12 after finishing the steps.
Substep 14) generating the last planting sequence: the reference data is then formed in accordance with substep 10) and the last planting sequence is formed.
For example: if the family of three is the family with the strongest ability to plant rice in villages, the agricultural activities of the last year are recorded as shown in the following table 1:
table 1 record of agronomic activity for three families to plant rice in the last year
By adopting the mode of the step S2, all planting process sequences of the family in the rice planting process are generated from the agricultural activity records, and the planting process sequences are shown in the following table 2:
table 2 three families planting the rice in the last year planting process sequence
And S3, recommending the optimal planting process sequence to other families for planting corresponding agricultural products in the village, and comparing and analyzing to remind the families which do not accord with the optimal planting process sequence to correct the agricultural activities.
S31, selecting a planting capacity indexIndex<1 as families with weak planting ability.
In a country, if the acre yield and the hourly yield of the agricultural products are below the average value, the family is poor in planting the agricultural products, so that the planting capacity index is usedIndex<1.
And S32, pushing the optimal planting process sequence to families with weak planting capacity in a subscription message service mode.
The subscription information is basically carried out once every year, after new planting energy is calculated, the subscription objects in the last year are cleared, and the subscription objects are current planting families with poor planting capacity of the agricultural products. From the slaveIndex<1, acquiring all contact modes of family members, and starting subscription message service for the family members. And pushing a planting process control image of the optimal planting process sequence and the home planting process sequence to enable the planting process control image to learn the planting process. The generation method of the family planting process sequence is the same as that of the optimal planting process sequence.
S33, monitoring the planting process, and judging whether the planting standard is met.
The method is characterized by comprising the steps of monitoring the agricultural activity process of families with weak planting capacity for corresponding types of agricultural products in real time, and mainly monitoring the planting process time and the agricultural material use condition.
Judging whether the optimal planting process sequence is met according to the time interval between the agricultural activities and the condition of various agricultural resource amounts in the agricultural activities. The judgment is mainly carried out in two aspects: the time interval between the agricultural activities and the various agricultural resource consumption conditions in the agricultural activities. For example: before the field is prepared, if the field is not prepared due to the expiration, judging that the field is not in accordance with the planting standard, otherwise judging that the field is in accordance with the planting standard; in the process of preparing the field, calculating the workload of preparing the field according to the acre number of the land, and judging whether the workload of the family accords with the workload based on the workload; after the field is finished, the optimal planting sequence requires that seedlings start after 7 days, and if the family advances or retards the seedlings, the seedlings are judged to be inconsistent. And in the seedling process, judging whether the seed consumption per mu meets 3 kg/mu. If not, the process proceeds to step S34.
34. And carrying out planting non-standard early warning on families which do not accord with the optimal planting process sequence, and reminding the corresponding families to correct the planting process.
If the planting process of the family with weak planting capacity is found to be not in accordance with the optimal planting standard in the real-time monitoring, the message prompt is immediately sent to the village commission, and the village commission is caused to prompt the village commission to correct the planting process.
S35, judging whether all the planting processes of the agricultural products are completed, if not, indicating that the planting processes are not completed, and continuing to monitor; otherwise, the method shows that all the planting processes of the agricultural products are completed in the year, and the data generated in the planting process can provide data support for the hand judgment and application of the planting energy of the next year.
After the agricultural product is planted, the yield data and the data of all agricultural activities are used for providing data support for the next judging planting energy, new optimal planting energy can be generated, and the capability of planting the agricultural products in the village can be continuously improved.
According to the invention, a standard planting process sequence can be integrated according to the planting process of the planting energy family, and the family with weak planting ability can conveniently learn the planting process by pushing the planting process sequence and the comparison image. And the optimal planting process is compared with families with weak planting capability, whether the optimal planting process is consistent with the optimal planting process sequence is judged according to the time interval between family farm activities with weak planting capability and the condition of various farm materials in the farm activities, and for the non-conforming farm activities, a comparison image can be sent to carry out correction reminding, so that the planting process can be controlled more intuitively and finely, the learning difficulty of the planting process is reduced, and popularization and use are facilitated.
Corresponding to the embodiment of the method, the invention also provides a planting process recommendation system based on the digital village, which comprises the following steps:
and the planting energy hand screening module: the method is used for evaluating the planting capacity of each household in the village by using two indexes of the acre yield of the agricultural products and the hourly yield of the planted agricultural products, and screening out the planting energy families of various agricultural products in the village;
and a planting process extraction module: the method comprises the steps of taking a planting process of planting energy families of various agricultural products as an optimal planting process, extracting a time sequence of the optimal planting process and generating an optimal planting process sequence;
and a planting process recommending module: the method is used for recommending the optimal planting process sequence to other families for planting corresponding agricultural products in the village, comparing and analyzing the optimal planting process sequence, and reminding the families which do not accord with the optimal planting process sequence to correct the agricultural activities.
The system embodiments and the method embodiments are in one-to-one correspondence, and the brief description of the system embodiments is just to refer to the method embodiments.
The invention also discloses an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the memory stores program instructions executable by the processor that the processor invokes to implement the aforementioned methods of the present invention.
The invention also discloses a computer readable storage medium storing computer instructions for causing a computer to implement all or part of the steps of the methods of the embodiments of the invention. The storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, i.e., may be distributed over a plurality of network elements. One of ordinary skill in the art may select some or all of the modules according to actual needs without performing any inventive effort to achieve the objectives of the present embodiment.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. A digital rural-based planting process recommendation method, the method comprising:
evaluating the planting capacity of each household in the country by using two indexes of the acre yield of the agricultural products under the same land grade and the hourly yield of the planted agricultural products, and screening out the planting energy of each household in the country;
taking the planting process of the planting energy families of various agricultural products as an optimal planting process, extracting the time sequence of the optimal planting process and generating an optimal planting process sequence;
recommending the optimal planting process sequence to other families for planting corresponding agricultural products in the village, and comparing and analyzing the optimal planting process sequence to remind the families which do not accord with the optimal planting process sequence to correct the agricultural activities.
2. The method for recommending a planting process based on a digital rural area according to claim 1, wherein the evaluating the planting capacity of each household in the rural area by using two indexes of the acre yield of agricultural products under the same land grade and the hourly yield of the planted agricultural products, and the screening the planting capacity of each household in the rural area specifically comprises:
for certain agricultural products, calculating average acre yield and average hourly yield of each household by utilizing the agricultural product sales data and household agricultural activity records under the same land grade of the previous year;
calculating the planting capacity index of each family according to the average mu yield and the average hourly yield of each familyIndex：
Wherein, the liquid crystal display device comprises a liquid crystal display device,prepresents the average acre yield of the corresponding agricultural products planted in one family,
represents the average mu yield of the corresponding agricultural products planted in the country, < >>
Is the average hourly yield of a household planting of the corresponding agricultural product,/->
Representing the average hourly yield of planting the corresponding agricultural product in the country; />
Is the weight coefficient of average mu yield and ∈Ten->
Is a weight coefficient for average hourly production;
for the planting ability indexIndexSorting, and screening the families with the largest planting capacity indexes as planting capacity families in the last year.
3. The digital rural based planting process recommendation method according to claim 2, wherein calculating the average acre yield and average hourly yield of each household using the agricultural product sales data and the household agricultural activity records under the same land level of the previous year comprises:
collecting the agronomic activity records of farmers under the same land level, and storing the records in the form of a data table structure, wherein the records comprise family numbers, land numbers, family members, agronomic activity start time and agronomic activity end time;
summarizing labor time length by land number;
collecting land production records and storing the records in the form of a data table structure, wherein the records comprise family numbers, land numbers, agricultural products, areas and total yield;
the land number is used for associating the agricultural activity record, the labor duration and the land production record to form result data, and each piece of result data records the labor duration and the agricultural product yield data of a certain land of a family contractor;
according to the result data, gathering agronomic activity records by taking families as units, and calculating average acre yield and average hourly yield of each family; the average hourly production is equal to the total production divided by the total labor duration.
4. The method of claim 1, wherein the extracting the time series of the best planting process and generating the best planting process series specifically comprises:
the agricultural activity records of the corresponding agricultural products of the planting energy families are ordered according to the ascending order of time;
selecting two adjacent agronomic activity records in the sorted agronomic activity records, wherein the former record is reference data, the latter record is subsequent data, comparing the reference data with the subsequent data, and judging whether the agronomic activity is the same or not and the time difference between the two records is not more than 2 days;
if the same agronomic activity is performed and the time difference is not more than 2 days, two agronomic activity records belong to the same planting sequence, and a planting sequence is generated in a data accumulation mode; if the two agronomic activity records are different, the two agronomic activity records are respectively divided into different planting sequences;
and repeating the above processes to perform sequential cyclic judgment, forming all planting sequences for planting the corresponding agricultural products from the agronomic activity records, and synthesizing all the planting sequences to form an optimal planting process sequence.
5. The method of claim 4, wherein the generating a planting sequence in the form of data accumulation comprises:
calculating labor time and agricultural material consumption from the reference data, and attaching the land number, the labor time and the agricultural material consumption in the reference data to corresponding data in the subsequent data; the labor time and the agricultural material consumption are directly added to the corresponding data, and if the land numbers are different, the land numbers are added after the land numbers of the subsequent data through the separator;
the number of land acres is obtained through land numbering, the labor capacity per acre and the agricultural material consumption per acre are calculated, and a planting sequence is generated.
6. The digital rural based growing process recommendation method of claim 2, wherein recommending the optimal growing process sequence to other households in the rural area for growing the corresponding agricultural products specifically comprises:
and selecting a family with a planting capacity Index of <1 as a family with weak planting capacity, and pushing a planting process comparison image of the optimal planting process sequence and the family planting process sequence to the family with weak planting capacity in a form of subscribing message service.
7. The digital rural based planting process recommendation method according to claim 6, wherein the performing the comparison analysis to remind households not conforming to the optimal planting process sequence to perform farming activity correction specifically comprises:
real-time monitoring the agronomic activity process of the corresponding agricultural products of families with weak planting capacity, and judging whether the family meets the optimal planting process sequence according to the time interval between agronomic activities and the condition of various agronomic resource consumption in the agronomic activities;
and carrying out planting non-standard early warning on families which do not accord with the optimal planting process sequence, and reminding the corresponding families to correct the planting process.
8. A digital rural-based planting process recommendation system, the system comprising:
and the planting energy hand screening module: the method is used for evaluating the planting capacity of each household in the village by using two indexes of the acre yield of the agricultural products and the hourly yield of the planted agricultural products, and screening out the planting energy families of various agricultural products in the village;
and a planting process extraction module: the method comprises the steps of taking a planting process of planting energy families of various agricultural products as an optimal planting process, extracting a time sequence of the optimal planting process and generating an optimal planting process sequence;
and a planting process recommending module: the method is used for recommending the optimal planting process sequence to other families for planting corresponding agricultural products in the village, comparing and analyzing the optimal planting process sequence, and reminding the families which do not accord with the optimal planting process sequence to correct the agricultural activities.
9. An electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete communication with each other through the bus;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to implement the method of any of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a computer to implement the method of any one of claims 1 to 7.
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